Conventional approaches that have been developed to characterize landing hazards in real-time generally involve using simple landing spacecraft models and the setting of thresholds to accommodate noise issues in hazard assessment. For example, the lidar-based Terrain Sensing and Recognition Algorithms (TSAR) developed for the Autonomous Landing Hazard Avoidance Technology (ALHAT) Project represented the lander by a planar patch (denoted Vehicle Footprint Dispersion Ellipse (VFDE)) equivalent to the diameter of the lander plus a measure of navigation error. The VFDE typically spans about 20 meters for an Altair class, 15 meter diameter, lunar lander.
Also, such earlier approaches require setting a number of detection thresholds in order to determine what sensed surface features correspond to surface hazards. Thresholds are typically determined by sandbox analyses and by Monte Carlo simulations. Since the approaches perform binary classification of terrain using thresholds, these approaches are deterministic (i.e., non-probabilistic) in nature. Such non-probabilistic approaches, however, fail to provide a robust method to detect hazards in the presence of sensor noise. The conventional methods require setting thresholds conservatively in order to avoid missing hazards (false negatives), but at the expense of introducing false alarms (false positives), thus significantly reducing the number of available safe landing sites. Furthermore, when dealing with significant noise levels, there may be no safe sites identified because these methods lack a formal interpretation and quantification of sensor noise. Accordingly, a novel approach that incorporates sensor noise modeling to more accurately identify safe landing sites may be beneficial.